A statistical approach for array CGH data analysis

BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome

Cilia and Obesity

The ciliopathies Bardet-Biedl syndrome and Alström syndrome cause obesity. How ciliary dysfunction leads to obesity has remained mysterious, partly because of a lack of understanding of the physiological roles of primary cilia in the organs and pathways involved in the regulation of metabolism and energy homeostasis. Historically, the study of rare monogenetic disorders that present with obesity has informed our molecular understanding of the mechanisms involved in nonsyndromic forms of obesity. Here, we present a framework, based on genetic studies in mice and humans, of the molecular and cellular pathways underlying long-term regulation of energy homeostasis. We focus on recent progress linking these pathways to the function of the primary cilia with a particular emphasis on the roles of neuronal primary cilia in the regulation of satiety

Genomic and epigenomic mapping of leptin-responsive neuronal populations involved in body weight regulation.

Genome wide association studies (GWAS) in obesity have identified a large number of noncoding loci located near genes expressed in the central nervous system. However, due to the difficulties in isolating and characterizing specific neuronal subpopulations, few obesity-associated SNPs have been functionally characterized. Leptin responsive neurons in the hypothalamus are essential in controlling energy homeostasis and body weight. Here, we combine FACS-sorting of leptin-responsive hypothalamic neuron nuclei with genomic and epigenomic approaches (RNA-seq, ChIP-seq, ATAC-seq) to generate a comprehensive map of leptin-response specific regulatory elements, several of which overlap obesity-associated GWAS variants. We demonstrate the usefulness of our leptin-response neuron regulome, by functionally characterizing a novel enhancer near Socs3, a leptin response-associated transcription factor. We envision our data to serve as a useful resource and a blueprint for functionally characterizing obesity-associated SNPs in the hypothalamus

Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity.

Most monogenic cases of obesity in humans have been linked to mutations in genes encoding members of the leptin-melanocortin pathway. Specifically, mutations in MC4R, the melanocortin-4 receptor gene, account for 3-5% of all severe obesity cases in humans1-3. Recently, ADCY3 (adenylyl cyclase 3) gene mutations have been implicated in obesity4,5. ADCY3 localizes to the primary cilia of neurons 6 , organelles that function as hubs for select signaling pathways. Mutations that disrupt the functions of primary cilia cause ciliopathies, rare recessive pleiotropic diseases in which obesity is a cardinal manifestation 7 . We demonstrate that MC4R colocalizes with ADCY3 at the primary cilia of a subset of hypothalamic neurons, that obesity-associated MC4R mutations impair ciliary localization and that inhibition of adenylyl cyclase signaling at the primary cilia of these neurons increases body weight. These data suggest that impaired signaling from the primary cilia of MC4R neurons is a common pathway underlying genetic causes of obesity in humans

Evolutionary aspects in evaluating mutations in the melanocortin 4 receptor

More than 70 missense mutations have been identified in the human melanocortin 4 receptor (MC4R), and many of them have been associated with obesity. In a number of cases, the causal link between mutations in MC4R and obesity is controversially discussed. Here, we mined evolution as an additional source of structural information that may help to evaluate the functional relevance of naturally occurring variations in MC4R. The sequence information of more than 60 MC4R orthologs enabled us to identify residues that are important for maintaining receptor function. More than 90% of all inactivating mutations found in obese patients were located at amino acid positions that are highly conserved during 450 million years of MC4R evolution in vertebrates. However, for a reasonable number of MC4R variants, we found no correlation between structural conservation of the mutated position and the reported functional consequence. By re-evaluating selected mutations in the MC4R, we demonstrate the usefulness of combining functional and evolutionary approaches

A statistical approach for CGH microarray data analysis

Microarray-CGH experiments aim at detecting and mapping chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample. Probes are constituted by sequences of genomic DNA (BACs) that are mapped on the genome. For this reason, the signal has a spatial coherence that has to be handled by specific statistical tools. Process segmentation seems to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number in average. We model a CGH profile by a random gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problem arise: the estimation of the break-points coordinates and the estimation of the number of segments. A dynamic programming algorithm is used to partition the data into a finite number of segments. A model selection approach is used to determine the number of segments in the profile, using an adaptative method. We explain why classical penalized criteria can not be used in the context of break-points detection and show the potentialities of our methodology, using publicly available data sets. We detect previously mapped chromosomal aberrations and disuss the performance of our methodology on noisier data concerning breast cancer cell lines

Engineering the Melanocortin-4 Receptor to Control Constitutive and Ligand-Mediated Gs Signaling In Vivo

The molecular and functional diversity of G protein–coupled receptors is essential to many physiological processes. However, this diversity presents a significant challenge to understanding the G protein–mediated signaling events that underlie a specific physiological response. To increase our understanding of these processes, we sought to gain control of the timing and specificity of Gs signaling in vivo. We used naturally occurring human mutations to develop two Gs-coupled engineered receptors that respond solely to a synthetic ligand (RASSLs). Our Gs-coupled RASSLs are based on the melanocortin-4 receptor, a centrally expressed receptor that plays an important role in the regulation of body weight. These RASSLs are not activated by the endogenous hormone α-melanocyte-stimulating hormone but respond potently to a selective synthetic ligand, tetrahydroisoquinoline. The RASSL variants reported here differ in their intrinsic basal activities, allowing the separation of the effects of basal signaling from ligand-mediated activation of the Gs pathway in vivo. These RASSLs can be used to activate Gs signaling in any tissue, but would be particularly useful for analyzing downstream events that mediate body weight regulation in mice. Our study also demonstrates the use of human genetic variation for protein engineering

Weight Loss after Roux-en-Y Gastric Bypass in Obese Patients Heterozygous for MC4R Mutations

BackgroundHeterozygous mutations in melanocortin-4 receptor (MC4R) are the most frequent genetic cause of obesity. Bariatric surgery is a successful treatment for severe obesity. The mechanisms of weight loss after bariatric surgery are not well understood.MethodsNinety-two patients who had Roux-en-Y gastric bypass (RYGB) surgery were screened for MC4R mutations. We compared percent excess weight loss (%EWL) in the four MC4R mutation carriers with that of two control groups: 8 matched controls and with the remaining 80 patients who underwent RYGB.ResultsFour patients were heterozygous for functionally significant MC4R mutations. In patients with MC4R mutations, the %EWL after RYGB (66% EWL) was not significantly different compared to matched controls (70% EWL) and non-matched controls (60% EWL) after 1 year of follow-up.ConclusionsThis study suggests that patients with heterozygous MC4R mutations also benefit from RYGB and that weight loss may be independent of the presence of such mutations

Lack of Support for the Association between GAD2 Polymorphisms and Severe Human Obesity

The demonstration of association between common genetic variants and chronic human diseases such as obesity could have profound implications for the prediction, prevention, and treatment of these conditions. Unequivocal proof of such an association, however, requires independent replication of initial positive findings. Recently, three (−243 A>G, +61450 C>A, and +83897 T>A) single nucleotide polymorphisms (SNPs) within glutamate decarboxylase 2 (GAD2) were found to be associated with class III obesity (body mass index > 40 kg/m(2)). The association was observed among 188 families (612 individuals) segregating the condition, and a case-control study of 575 cases and 646 lean controls. Functional data supporting a pathophysiological role for one of the SNPs (−243 A>G) were also presented. The gene GAD2 encodes the 65-kDa subunit of glutamic acid decarboxylase—GAD65. In the present study, we attempted to replicate this association in larger groups of individuals, and to extend the functional studies of the −243 A>G SNP. Among 2,359 individuals comprising 693 German nuclear families with severe, early-onset obesity, we found no evidence for a relationship between the three GAD2 SNPs and obesity, whether SNPs were studied individually or as haplotypes. In two independent case-control studies (a total of 680 class III obesity cases and 1,186 lean controls), there was no significant relationship between the −243 A>G SNP and obesity (OR = 0.99, 95% CI 0.83–1.18, p = 0.89) in the pooled sample. These negative findings were recapitulated in a meta-analysis, incorporating all published data for the association between the −243G allele and class III obesity, which yielded an OR of 1.11 (95% CI 0.90–1.36, p = 0.28) in a total sample of 1,252 class III obese cases and 1,800 lean controls. Moreover, analysis of common haplotypes encompassing the GAD2 locus revealed no association with severe obesity in families with the condition. We also obtained functional data for the −243 A>G SNP that does not support a pathophysiological role for this variant in obesity. Potential confounding variables in association studies involving common variants and complex diseases (low power to detect modest genetic effects, overinterpretation of marginal data, population stratification, and biological plausibility) are also discussed in the context of GAD2 and severe obesity

Genetic association study of adiposity and melanocortin-4 receptor (MC4R) common variants: Replication and functional characterization of non-coding regions

Common genetic variants 3′ of MC4R within two large linkage disequilibrium (LD) blocks spanning 288 kb have been associated with common and rare forms of obesity. This large association region has not been refined and the relevant DNA segments within the association region have not been identified. In this study, we investigated whether common variants in the MC4R gene region were associated with adiposity-related traits in a biracial population-based study. Single nucleotide polymorphisms (SNPs) in the MC4R region were genotyped with a custom array and a genome-wide array and associations between SNPs and five adiposity-related traits were determined using race-stratified linear regression. Previously reported associations between lower BMI and the minor alleles of rs2229616/Val103Ile and rs52820871/Ile251Leu were replicated in white female participants. Among white participants, rs11152221 in a proximal 3′ LD block (closer to MC4R) was significantly associated with multiple adiposity traits, but SNPs in a distal 309 LD block (farther from MC4R ) were not. In a case-control study of severe obesity, rs11152221 was significantly associated. The association results directed our follow-up studies to the proximal LD block downstream of MC4R. By considering nucleotide conservation, the significance of association, and proximity to the MC4R gene, we identified a candidate MC4R regulatory region. This candidate region was sequenced in 20 individuals from a study of severe obesity in an attempt to identify additional variants, and the candidate region was tested for enhancer activity using in vivo enhancer assays in zebrafish and mice. Novel variants were not identified by sequencing and the candidate region did not drive reporter gene expression in zebrafish or mice. The identification of a putative insulator in this region could help to explain the challenges faced in this study and others to link SNPs associated with adiposity to altered MC4R expression. © 2014 Evans et al